code: def create_fast_rcnn_eval_model(
      model, image_input, roi_proposals, cfg
  ):
      print("creating eval model")
      predictor = clone_model(
          model,
          [cfg["MODEL"].FEATURE_NODE_NAME, "roi_proposals"],
          ["cls_score", "bbox_regr"],
          CloneMethod.freeze,
      )
      pred_net = predictor(image_input, roi_proposals)
      cls_score = pred_net.outputs[0]
      bbox_regr = pred_net.outputs[1]
  
      if cfg.BBOX_NORMALIZE_TARGETS:
          num_boxes = int(bbox_regr.shape[1] / 4)
          bbox_normalize_means = np.array(
              cfg.BBOX_NORMALIZE_MEANS * num_boxes
          )
          bbox_normalize_stds = np.array(
              cfg.BBOX_NORMALIZE_STDS * num_boxes
          )
          bbox_regr = plus(
              element_times(bbox_regr, bbox_normalize_stds),
              bbox_normalize_means,
              name="bbox_regr",
          )
  
      cls_pred = softmax(cls_score, axis=1, name="cls_pred")
      eval_model = combine([cls_pred, bbox_regr])
  
      if cfg["CNTK"].DEBUG_OUTPUT:
          plot(
              eval_model,
              os.path.join(
                  cfg.OUTPUT_PATH,
                  "graph_frcn_eval." + cfg["CNTK"].GRAPH_TYPE,
              ),
          )
  
      return eval_model
  
imports: from __future__ import print_function
  import numpy
  import os, sys
  import argparse
  import easydict
  import cntk
  from cntk import Trainer, load_model, Axis, input_variable, parameter, times, combine, softmax, roipooling, plus, element_times, CloneMethod, alias, Communicator, reduce_sum
  from cntk.core import Value
  from cntk.initializer import normal
  from cntk.layers import placeholder, Constant, Sequential
  from cntk.learners import momentum_sgd, learning_parameter_schedule_per_sample, momentum_schedule
  from cntk.logging import log_number_of_parameters, ProgressPrinter
  from cntk.logging.graph import find_by_name, plot
  from cntk.losses import cross_entropy_with_softmax
  from cntk.metrics import classification_error
  from _cntk_py import force_deterministic_algorithms
  from utils.rpn.cntk_smoothL1_loss import SmoothL1Loss
  from utils.annotations.annotations_helper import parse_class_map_file
  from utils.od_mb_source import ObjectDetectionMinibatchSource
  from utils.proposal_helpers import ProposalProvider
label_desc: Use image node_id 8 for calling a global function with example usage: create_fast_rcnn_eval_model(model, image_input, roi_proposals, cfg) and returns: eval_model